{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "297fd093",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2471e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82d18f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>group</th>\n",
       "      <th>F0160</th>\n",
       "      <th>F0196</th>\n",
       "      <th>F0249</th>\n",
       "      <th>F0297</th>\n",
       "      <th>F0307</th>\n",
       "      <th>F0343</th>\n",
       "      <th>F0346</th>\n",
       "      <th>F0360</th>\n",
       "      <th>...</th>\n",
       "      <th>F1642</th>\n",
       "      <th>F1644</th>\n",
       "      <th>F1654</th>\n",
       "      <th>F1661</th>\n",
       "      <th>F1664</th>\n",
       "      <th>F1673</th>\n",
       "      <th>F1678</th>\n",
       "      <th>F1679</th>\n",
       "      <th>F1680</th>\n",
       "      <th>F1681</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P_1_IgAN</td>\n",
       "      <td>IgAN</td>\n",
       "      <td>1.142403e+08</td>\n",
       "      <td>7.070960e+07</td>\n",
       "      <td>9775631556</td>\n",
       "      <td>135138804.9</td>\n",
       "      <td>1.143517e+09</td>\n",
       "      <td>9049868316</td>\n",
       "      <td>1.013420e+09</td>\n",
       "      <td>1.133656e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>25230043.95</td>\n",
       "      <td>111232078.7</td>\n",
       "      <td>237458451.2</td>\n",
       "      <td>2795789769</td>\n",
       "      <td>157904015.1</td>\n",
       "      <td>8.011176e+07</td>\n",
       "      <td>180489097.7</td>\n",
       "      <td>346097649.9</td>\n",
       "      <td>20581746.00</td>\n",
       "      <td>308064123.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P_10_IgAN</td>\n",
       "      <td>IgAN</td>\n",
       "      <td>9.702087e+07</td>\n",
       "      <td>1.332498e+08</td>\n",
       "      <td>13287215585</td>\n",
       "      <td>137384501.8</td>\n",
       "      <td>1.235676e+09</td>\n",
       "      <td>12568463232</td>\n",
       "      <td>1.162556e+09</td>\n",
       "      <td>8.380902e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>32868215.86</td>\n",
       "      <td>186951046.0</td>\n",
       "      <td>296694075.7</td>\n",
       "      <td>4724555250</td>\n",
       "      <td>126230251.8</td>\n",
       "      <td>1.521238e+08</td>\n",
       "      <td>258735522.3</td>\n",
       "      <td>393102408.7</td>\n",
       "      <td>10933723.08</td>\n",
       "      <td>473984275.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P_100_MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>1.122328e+08</td>\n",
       "      <td>9.994140e+07</td>\n",
       "      <td>12815602853</td>\n",
       "      <td>154339982.0</td>\n",
       "      <td>9.648844e+08</td>\n",
       "      <td>11662704481</td>\n",
       "      <td>8.655766e+08</td>\n",
       "      <td>1.148080e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>29833389.98</td>\n",
       "      <td>188404058.6</td>\n",
       "      <td>238466281.4</td>\n",
       "      <td>3256837228</td>\n",
       "      <td>109389020.1</td>\n",
       "      <td>1.506269e+08</td>\n",
       "      <td>247240213.4</td>\n",
       "      <td>444571313.2</td>\n",
       "      <td>24471534.49</td>\n",
       "      <td>457901009.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P_101_DMN</td>\n",
       "      <td>DMN</td>\n",
       "      <td>1.178547e+08</td>\n",
       "      <td>1.389706e+08</td>\n",
       "      <td>12203406144</td>\n",
       "      <td>129277433.8</td>\n",
       "      <td>1.194023e+09</td>\n",
       "      <td>12549108656</td>\n",
       "      <td>1.094586e+09</td>\n",
       "      <td>9.197134e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>41327077.16</td>\n",
       "      <td>204842775.8</td>\n",
       "      <td>157394205.5</td>\n",
       "      <td>8343410427</td>\n",
       "      <td>105271671.3</td>\n",
       "      <td>1.420370e+08</td>\n",
       "      <td>322140375.2</td>\n",
       "      <td>440519808.1</td>\n",
       "      <td>16969190.44</td>\n",
       "      <td>862627773.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P_102_IgAN</td>\n",
       "      <td>IgAN</td>\n",
       "      <td>1.063148e+08</td>\n",
       "      <td>5.724365e+07</td>\n",
       "      <td>12980955663</td>\n",
       "      <td>135828348.7</td>\n",
       "      <td>1.163812e+09</td>\n",
       "      <td>12864754434</td>\n",
       "      <td>1.050891e+09</td>\n",
       "      <td>1.168102e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>35880636.17</td>\n",
       "      <td>161873888.3</td>\n",
       "      <td>239592031.1</td>\n",
       "      <td>3438519432</td>\n",
       "      <td>116139725.2</td>\n",
       "      <td>1.069596e+08</td>\n",
       "      <td>265246172.8</td>\n",
       "      <td>365675436.4</td>\n",
       "      <td>23257639.62</td>\n",
       "      <td>476870829.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample group         F0160         F0196        F0249        F0297  \\\n",
       "0    P_1_IgAN  IgAN  1.142403e+08  7.070960e+07   9775631556  135138804.9   \n",
       "1   P_10_IgAN  IgAN  9.702087e+07  1.332498e+08  13287215585  137384501.8   \n",
       "2    P_100_MN    MN  1.122328e+08  9.994140e+07  12815602853  154339982.0   \n",
       "3   P_101_DMN   DMN  1.178547e+08  1.389706e+08  12203406144  129277433.8   \n",
       "4  P_102_IgAN  IgAN  1.063148e+08  5.724365e+07  12980955663  135828348.7   \n",
       "\n",
       "          F0307        F0343         F0346         F0360  ...        F1642  \\\n",
       "0  1.143517e+09   9049868316  1.013420e+09  1.133656e+08  ...  25230043.95   \n",
       "1  1.235676e+09  12568463232  1.162556e+09  8.380902e+07  ...  32868215.86   \n",
       "2  9.648844e+08  11662704481  8.655766e+08  1.148080e+08  ...  29833389.98   \n",
       "3  1.194023e+09  12549108656  1.094586e+09  9.197134e+07  ...  41327077.16   \n",
       "4  1.163812e+09  12864754434  1.050891e+09  1.168102e+08  ...  35880636.17   \n",
       "\n",
       "         F1644        F1654       F1661        F1664         F1673  \\\n",
       "0  111232078.7  237458451.2  2795789769  157904015.1  8.011176e+07   \n",
       "1  186951046.0  296694075.7  4724555250  126230251.8  1.521238e+08   \n",
       "2  188404058.6  238466281.4  3256837228  109389020.1  1.506269e+08   \n",
       "3  204842775.8  157394205.5  8343410427  105271671.3  1.420370e+08   \n",
       "4  161873888.3  239592031.1  3438519432  116139725.2  1.069596e+08   \n",
       "\n",
       "         F1678        F1679        F1680        F1681  \n",
       "0  180489097.7  346097649.9  20581746.00  308064123.6  \n",
       "1  258735522.3  393102408.7  10933723.08  473984275.7  \n",
       "2  247240213.4  444571313.2  24471534.49  457901009.5  \n",
       "3  322140375.2  440519808.1  16969190.44  862627773.7  \n",
       "4  265246172.8  365675436.4  23257639.62  476870829.8  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#데이터 불러오기\n",
    "dataset = pd.read_csv('./nephropathy_dataset.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9391c12e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#'sample' column은 학습에 관계없는 값이므로 drop\n",
    "dataset.drop(['sample'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa64d26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 286 entries, 0 to 285\n",
      "Columns: 244 entries, group to F1681\n",
      "dtypes: float64(231), int64(12), object(1)\n",
      "memory usage: 545.3+ KB\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>F0160</th>\n",
       "      <th>F0196</th>\n",
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       "      <th>F1679</th>\n",
       "      <th>F1680</th>\n",
       "      <th>F1681</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1.142403e+08</td>\n",
       "      <td>7.070960e+07</td>\n",
       "      <td>9775631556</td>\n",
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       "      <td>459687707.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>346097649.9</td>\n",
       "      <td>20581746.00</td>\n",
       "      <td>308064123.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.702087e+07</td>\n",
       "      <td>1.332498e+08</td>\n",
       "      <td>13287215585</td>\n",
       "      <td>137384501.8</td>\n",
       "      <td>1.235676e+09</td>\n",
       "      <td>12568463232</td>\n",
       "      <td>1.162556e+09</td>\n",
       "      <td>8.380902e+07</td>\n",
       "      <td>468104661.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32868215.86</td>\n",
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       "      <td>126230251.8</td>\n",
       "      <td>1.521238e+08</td>\n",
       "      <td>258735522.3</td>\n",
       "      <td>393102408.7</td>\n",
       "      <td>10933723.08</td>\n",
       "      <td>473984275.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.122328e+08</td>\n",
       "      <td>9.994140e+07</td>\n",
       "      <td>12815602853</td>\n",
       "      <td>154339982.0</td>\n",
       "      <td>9.648844e+08</td>\n",
       "      <td>11662704481</td>\n",
       "      <td>8.655766e+08</td>\n",
       "      <td>1.148080e+08</td>\n",
       "      <td>414994522.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29833389.98</td>\n",
       "      <td>188404058.6</td>\n",
       "      <td>238466281.4</td>\n",
       "      <td>3256837228</td>\n",
       "      <td>109389020.1</td>\n",
       "      <td>1.506269e+08</td>\n",
       "      <td>247240213.4</td>\n",
       "      <td>444571313.2</td>\n",
       "      <td>24471534.49</td>\n",
       "      <td>457901009.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.178547e+08</td>\n",
       "      <td>1.389706e+08</td>\n",
       "      <td>12203406144</td>\n",
       "      <td>129277433.8</td>\n",
       "      <td>1.194023e+09</td>\n",
       "      <td>12549108656</td>\n",
       "      <td>1.094586e+09</td>\n",
       "      <td>9.197134e+07</td>\n",
       "      <td>457563663.9</td>\n",
       "      <td>...</td>\n",
       "      <td>41327077.16</td>\n",
       "      <td>204842775.8</td>\n",
       "      <td>157394205.5</td>\n",
       "      <td>8343410427</td>\n",
       "      <td>105271671.3</td>\n",
       "      <td>1.420370e+08</td>\n",
       "      <td>322140375.2</td>\n",
       "      <td>440519808.1</td>\n",
       "      <td>16969190.44</td>\n",
       "      <td>862627773.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1.063148e+08</td>\n",
       "      <td>5.724365e+07</td>\n",
       "      <td>12980955663</td>\n",
       "      <td>135828348.7</td>\n",
       "      <td>1.163812e+09</td>\n",
       "      <td>12864754434</td>\n",
       "      <td>1.050891e+09</td>\n",
       "      <td>1.168102e+08</td>\n",
       "      <td>491612219.6</td>\n",
       "      <td>...</td>\n",
       "      <td>35880636.17</td>\n",
       "      <td>161873888.3</td>\n",
       "      <td>239592031.1</td>\n",
       "      <td>3438519432</td>\n",
       "      <td>116139725.2</td>\n",
       "      <td>1.069596e+08</td>\n",
       "      <td>265246172.8</td>\n",
       "      <td>365675436.4</td>\n",
       "      <td>23257639.62</td>\n",
       "      <td>476870829.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   group         F0160         F0196        F0249        F0297         F0307  \\\n",
       "0      2  1.142403e+08  7.070960e+07   9775631556  135138804.9  1.143517e+09   \n",
       "1      2  9.702087e+07  1.332498e+08  13287215585  137384501.8  1.235676e+09   \n",
       "2      3  1.122328e+08  9.994140e+07  12815602853  154339982.0  9.648844e+08   \n",
       "3      0  1.178547e+08  1.389706e+08  12203406144  129277433.8  1.194023e+09   \n",
       "4      2  1.063148e+08  5.724365e+07  12980955663  135828348.7  1.163812e+09   \n",
       "\n",
       "         F0343         F0346         F0360        F0365  ...        F1642  \\\n",
       "0   9049868316  1.013420e+09  1.133656e+08  459687707.0  ...  25230043.95   \n",
       "1  12568463232  1.162556e+09  8.380902e+07  468104661.0  ...  32868215.86   \n",
       "2  11662704481  8.655766e+08  1.148080e+08  414994522.0  ...  29833389.98   \n",
       "3  12549108656  1.094586e+09  9.197134e+07  457563663.9  ...  41327077.16   \n",
       "4  12864754434  1.050891e+09  1.168102e+08  491612219.6  ...  35880636.17   \n",
       "\n",
       "         F1644        F1654       F1661        F1664         F1673  \\\n",
       "0  111232078.7  237458451.2  2795789769  157904015.1  8.011176e+07   \n",
       "1  186951046.0  296694075.7  4724555250  126230251.8  1.521238e+08   \n",
       "2  188404058.6  238466281.4  3256837228  109389020.1  1.506269e+08   \n",
       "3  204842775.8  157394205.5  8343410427  105271671.3  1.420370e+08   \n",
       "4  161873888.3  239592031.1  3438519432  116139725.2  1.069596e+08   \n",
       "\n",
       "         F1678        F1679        F1680        F1681  \n",
       "0  180489097.7  346097649.9  20581746.00  308064123.6  \n",
       "1  258735522.3  393102408.7  10933723.08  473984275.7  \n",
       "2  247240213.4  444571313.2  24471534.49  457901009.5  \n",
       "3  322140375.2  440519808.1  16969190.44  862627773.7  \n",
       "4  265246172.8  365675436.4  23257639.62  476870829.8  \n",
       "\n",
       "[5 rows x 244 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label이 될 'group'을 정수형으로 인코딩(1. 어떠한 원인으로 신장질환이 발병했는지 분류)\n",
    "dataset.info()\n",
    "dataset['group'].replace('DMN', 0, inplace=True)\n",
    "dataset['group'].replace('HTN-N',1, inplace=True)\n",
    "dataset['group'].replace('IgAN',2, inplace=True)\n",
    "dataset['group'].replace('MN',3, inplace=True)\n",
    "dataset['group'].replace('Normal',4, inplace=True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0196da7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    66\n",
       "3    66\n",
       "4    66\n",
       "0    64\n",
       "1    24\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#각각의 label이 몇 개의 데이터로 이루어져있는지 확인\n",
    "dataset['group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e478a3b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    56\n",
      "4    55\n",
      "0    51\n",
      "3    45\n",
      "1    21\n",
      "Name: group, dtype: int64\n",
      "SMOTE 적용 전 학습용 피처/레이블 데이터 세트:  (228, 243) (228,)\n",
      "SMOTE 적용 후 학습용 피처/레이블 데이터 세트:  (280, 243) (280,)\n",
      "0    56\n",
      "2    56\n",
      "3    56\n",
      "4    56\n",
      "1    56\n",
      "Name: group, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#SMOTE 오버샘플링 적용\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = dataset.iloc[:,1:]\n",
    "y = dataset.iloc[:,0]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "print(y_train.value_counts())\n",
    "smote = SMOTE()\n",
    "X_train_over, y_train_over = smote.fit_resample(X_train, y_train)\n",
    "print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)\n",
    "print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)\n",
    "print(y_train_over.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f5d8ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#랜덤 포레스트 예측\n",
    "rf_clf = RandomForestClassifier(n_estimators = 1000, max_depth = 100)\n",
    "rf_clf.fit(X_train_over, y_train_over)\n",
    "pred = rf_clf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8644d3ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5862068965517241\n"
     ]
    }
   ],
   "source": [
    "print('accuracy : ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "744d7605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 20 entries, F0360 to F0554\n",
      "Series name: None\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "20 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 876.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#feature importance 분석\n",
    "ftr_importances_values = rf_clf.feature_importances_\n",
    "ftr_importances = pd.Series(ftr_importances_values, index = X_train_over.columns)\n",
    "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=ftr_top20, y=ftr_top20.index)\n",
    "plt.show()\n",
    "print(ftr_top20.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce76b88e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance가 압도적으로 높은 'F0360'을 시각적으로 분석\n",
    "\n",
    "dataset_0 = dataset[dataset['group']==0]\n",
    "dataset_1 = dataset[dataset['group']==1]\n",
    "dataset_2 = dataset[dataset['group']==2]\n",
    "dataset_3 = dataset[dataset['group']==3]\n",
    "dataset_4 = dataset[dataset['group']==4]\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(24,20), nrows=3, ncols = 2)\n",
    "\n",
    "sns.histplot(dataset['F0360'], kde=True, ax = axs[0][0])\n",
    "sns.histplot(dataset_0['F0360'], kde=True, ax = axs[0][1])\n",
    "sns.histplot(dataset_1['F0360'], kde=True, ax = axs[1][0])\n",
    "sns.histplot(dataset_2['F0360'], kde=True, ax = axs[1][1])\n",
    "sns.histplot(dataset_3['F0360'], kde=True, ax = axs[2][0])\n",
    "sns.histplot(dataset_4['F0360'], kde=True, ax = axs[2][1])\n",
    "                                                       \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f384371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64.0</td>\n",
       "      <td>1.128998e+08</td>\n",
       "      <td>1.652533e+07</td>\n",
       "      <td>5.321132e+07</td>\n",
       "      <td>1.066947e+08</td>\n",
       "      <td>1.162999e+08</td>\n",
       "      <td>1.243336e+08</td>\n",
       "      <td>132885177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.210607e+08</td>\n",
       "      <td>1.042182e+07</td>\n",
       "      <td>8.370603e+07</td>\n",
       "      <td>1.194557e+08</td>\n",
       "      <td>1.244597e+08</td>\n",
       "      <td>1.268625e+08</td>\n",
       "      <td>132348152.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1.189002e+08</td>\n",
       "      <td>9.741092e+06</td>\n",
       "      <td>8.092749e+07</td>\n",
       "      <td>1.144408e+08</td>\n",
       "      <td>1.205240e+08</td>\n",
       "      <td>1.243885e+08</td>\n",
       "      <td>132565241.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1.083133e+08</td>\n",
       "      <td>1.194606e+07</td>\n",
       "      <td>7.884944e+07</td>\n",
       "      <td>1.019300e+08</td>\n",
       "      <td>1.097523e+08</td>\n",
       "      <td>1.163286e+08</td>\n",
       "      <td>132198693.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1.293493e+08</td>\n",
       "      <td>4.796080e+06</td>\n",
       "      <td>1.028602e+08</td>\n",
       "      <td>1.273536e+08</td>\n",
       "      <td>1.300557e+08</td>\n",
       "      <td>1.319831e+08</td>\n",
       "      <td>137770674.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count          mean           std           min           25%  \\\n",
       "group                                                                  \n",
       "0       64.0  1.128998e+08  1.652533e+07  5.321132e+07  1.066947e+08   \n",
       "1       24.0  1.210607e+08  1.042182e+07  8.370603e+07  1.194557e+08   \n",
       "2       66.0  1.189002e+08  9.741092e+06  8.092749e+07  1.144408e+08   \n",
       "3       66.0  1.083133e+08  1.194606e+07  7.884944e+07  1.019300e+08   \n",
       "4       66.0  1.293493e+08  4.796080e+06  1.028602e+08  1.273536e+08   \n",
       "\n",
       "                50%           75%          max  \n",
       "group                                           \n",
       "0      1.162999e+08  1.243336e+08  132885177.0  \n",
       "1      1.244597e+08  1.268625e+08  132348152.2  \n",
       "2      1.205240e+08  1.243885e+08  132565241.2  \n",
       "3      1.097523e+08  1.163286e+08  132198693.6  \n",
       "4      1.300557e+08  1.319831e+08  137770674.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'F0360'의 통계적 분석\n",
    "dataset.groupby('group')['F0360'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e036e480",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jinsung\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM정확도:  0.7069\n"
     ]
    }
   ],
   "source": [
    "#LightGBM을 이용한 분류\n",
    "lgbm_wrapper = LGBMClassifier(n_estimators=1000, learning_rate=0.05)\n",
    "lgbm_wrapper.fit(X_train_over, y_train_over, verbose=True)\n",
    "preds = lgbm_wrapper.predict(X_test)\n",
    "score = accuracy_score(y_test, preds)\n",
    "print(\"LGBM정확도: \", np.round(score,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3ad04a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('./nephropathy_dataset.csv')\n",
    "\n",
    "#성능지표 함수 선언\n",
    "\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, f1_score\n",
    "\n",
    "def get_clf_eval(y_test, pred):\n",
    "    confusion = confusion_matrix(y_test, pred)\n",
    "    accuracy = accuracy_score(y_test, pred)\n",
    "    precision = precision_score(y_test, pred)\n",
    "    recall = recall_score(y_test, pred)\n",
    "    f1 = f1_score(y_test,pred)\n",
    "\n",
    "    print('오차 행렬')\n",
    "    print(confusion)\n",
    "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f}, F1:{3:.4f}'.format(accuracy,precision, recall,f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33112e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 286 entries, 0 to 285\n",
      "Columns: 244 entries, group to F1681\n",
      "dtypes: float64(231), int64(12), object(1)\n",
      "memory usage: 545.3+ KB\n"
     ]
    },
    {
     "data": {
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       "      <td>12815602853</td>\n",
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       "      <td>414994522.0</td>\n",
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      ],
      "text/plain": [
       "   group         F0160         F0196        F0249        F0297         F0307  \\\n",
       "0      1  1.142403e+08  7.070960e+07   9775631556  135138804.9  1.143517e+09   \n",
       "1      1  9.702087e+07  1.332498e+08  13287215585  137384501.8  1.235676e+09   \n",
       "2      1  1.122328e+08  9.994140e+07  12815602853  154339982.0  9.648844e+08   \n",
       "3      1  1.178547e+08  1.389706e+08  12203406144  129277433.8  1.194023e+09   \n",
       "4      1  1.063148e+08  5.724365e+07  12980955663  135828348.7  1.163812e+09   \n",
       "\n",
       "         F0343         F0346         F0360        F0365  ...        F1642  \\\n",
       "0   9049868316  1.013420e+09  1.133656e+08  459687707.0  ...  25230043.95   \n",
       "1  12568463232  1.162556e+09  8.380902e+07  468104661.0  ...  32868215.86   \n",
       "2  11662704481  8.655766e+08  1.148080e+08  414994522.0  ...  29833389.98   \n",
       "3  12549108656  1.094586e+09  9.197134e+07  457563663.9  ...  41327077.16   \n",
       "4  12864754434  1.050891e+09  1.168102e+08  491612219.6  ...  35880636.17   \n",
       "\n",
       "         F1644        F1654       F1661        F1664         F1673  \\\n",
       "0  111232078.7  237458451.2  2795789769  157904015.1  8.011176e+07   \n",
       "1  186951046.0  296694075.7  4724555250  126230251.8  1.521238e+08   \n",
       "2  188404058.6  238466281.4  3256837228  109389020.1  1.506269e+08   \n",
       "3  204842775.8  157394205.5  8343410427  105271671.3  1.420370e+08   \n",
       "4  161873888.3  239592031.1  3438519432  116139725.2  1.069596e+08   \n",
       "\n",
       "         F1678        F1679        F1680        F1681  \n",
       "0  180489097.7  346097649.9  20581746.00  308064123.6  \n",
       "1  258735522.3  393102408.7  10933723.08  473984275.7  \n",
       "2  247240213.4  444571313.2  24471534.49  457901009.5  \n",
       "3  322140375.2  440519808.1  16969190.44  862627773.7  \n",
       "4  265246172.8  365675436.4  23257639.62  476870829.8  \n",
       "\n",
       "[5 rows x 244 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label이 될 'group'을 정수형으로 인코딩(2. 환자(1)와 일반인(1)만을 분류) \n",
    "dataset.head()\n",
    "dataset.drop(['sample'], axis=1, inplace = True)\n",
    "dataset.info()\n",
    "dataset['group'].replace('DMN', 1, inplace=True)\n",
    "dataset['group'].replace('HTN-N',1, inplace=True)\n",
    "dataset['group'].replace('IgAN',1, inplace=True)\n",
    "dataset['group'].replace('MN',1, inplace=True)\n",
    "dataset['group'].replace('Normal',0, inplace=True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2f74def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    176\n",
      "0     52\n",
      "Name: group, dtype: int64\n",
      "SMOTE 적용 전 학습용 피처/레이블 데이터 세트:  (228, 243) (228,)\n",
      "SMOTE 적용 후 학습용 피처/레이블 데이터 세트:  (352, 243) (352,)\n",
      "1    176\n",
      "0    176\n",
      "Name: group, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#SMOTE 오버샘플링 적용\n",
    "X = dataset.iloc[:,1:]\n",
    "y = dataset.iloc[:,0]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "print(y_train.value_counts())\n",
    "smote = SMOTE()\n",
    "X_train_over, y_train_over = smote.fit_resample(X_train, y_train)\n",
    "print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)\n",
    "print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)\n",
    "print(y_train_over.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9778b389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차 행렬\n",
      "[[11  3]\n",
      " [ 1 43]]\n",
      "정확도: 0.9310, 정밀도: 0.9348, 재현율: 0.9773, F1:0.9556\n"
     ]
    }
   ],
   "source": [
    "#랜덤 포레스트\n",
    "rf_clf = RandomForestClassifier(n_estimators = 1000, max_depth = 100)\n",
    "rf_clf.fit(X_train_over, y_train_over)\n",
    "pred = rf_clf.predict(X_test)\n",
    "get_clf_eval(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e90fa1dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 20 entries, F0426 to F0798\n",
      "Series name: None\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "20 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 876.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#feature importance\n",
    "ftr_importances_values = rf_clf.feature_importances_\n",
    "ftr_importances = pd.Series(ftr_importances_values, index = X_train_over.columns)\n",
    "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=ftr_top20, y=ftr_top20.index)\n",
    "plt.show()\n",
    "print(ftr_top20.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "39bf34c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jinsung\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차 행렬\n",
      "[[13  1]\n",
      " [ 0 44]]\n",
      "정확도: 0.9828, 정밀도: 0.9778, 재현율: 1.0000, F1:0.9888\n"
     ]
    }
   ],
   "source": [
    "#LightGBM 이용\n",
    "lgbm_wrapper = LGBMClassifier(n_estimators=1000, learning_rate=0.05, max_depth = 500)\n",
    "lgbm_wrapper.fit(X_train_over, y_train_over, verbose=True)\n",
    "preds = lgbm_wrapper.predict(X_test)\n",
    "score = accuracy_score(y_test, preds)\n",
    "get_clf_eval(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48aa9498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(dataset['F0360'], kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b5f28c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_0 = dataset[dataset['group']==0]\n",
    "dataset_1 = dataset[dataset['group']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8311dbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#'F0360'시각적 분석\n",
    "fig, axs = plt.subplots(figsize=(24,10), nrows=1, ncols = 2)\n",
    "fig.tight_layout()\n",
    "sns.histplot(dataset_0['F0360'], kde=True, ax = axs[0])\n",
    "sns.histplot(dataset_1['F0360'], kde=True, ax = axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d88ec4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b72654f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
